Irrigators' Associations (IAs) are critical in managing water resources and influencing agricultural productivity and rural sustainability. Predicting IA membership is complex due to socio-economic, organizational, and environmental factors. This study applies machine learning techniques—Decision Trees and Random Forests— to model IA membership and identify the key predictive variables. Using a dataset of 234 IA records, the models were evaluated based on Mean Absolute Error (MAE), with the Random Forest model achieving a lower MAE of 19.49, compared to 24.92 for the Decision Tree. Key predictors include the number of farm beneficiaries, service area, years of operation, and leadership structure. The results demonstrate the potential of machine learning in supporting data-driven planning for IA engagement, offering valuable insights that can enhance resource allocation and inform policy development in the agricultural sector, ultimately contributing to more efficient water management and improved rural livelihoods.

Keywords: Irrigators' Associations, Machine Learning, Decision Trees, Random Forests, Mean Absolute Error